如何将张板可视化与tf.stimator集成

Question

我有经典的Tensorflow代码，用于识别手写数字https://github.com/tensorflow/tensorflow/tensorflow/tensorflow/blob/master/master/tensorflow/tensorflow/tutorials/tutorials/mnist/mnist/mnist_with_summaries.py，.ESTIMATOR。我的问题是复杂的，由两个问题组成

1. 我应该为代码中的目标变量编写tf.summary（）以可视化tensoboard中的数据，而仅键入 tensorboard -- logdir=/tmp/mnist_convnet_model或tf.stimator。estimator在 */tmp/mnist_convnet_model目录中自动收集所有摘要，而我只能调用 tensorboard -- logdir=/tmp/mnist_convnet_model？

2. 如果我必须写tf.summary()，您是否可以回答我，我是否应该在代码中插入tf summary merge_all()以及在代码中插入什么代码？

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.INFO)

def cnn_model_fn(features, labels, mode):
  """Model function for CNN."""
  # Input Layer
  input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
  # Convolutional Layer #1
  conv1 = tf.layers.conv2d(
      inputs=input_layer,
      filters=32,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu)
  # Pooling Layer #1
  pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
  # Convolutional Layer #2
  conv2 = tf.layers.conv2d(
      inputs=pool1,
      filters=64,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu)
  # Pooling Layer #2
  pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
  # Flatten tensor into a batch of vectors
  pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
  # Dense Layer
  dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
  # Add dropout operation; 0.6 probability that element will be kept
  dropout = tf.layers.dropout(
      inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
  # Logits layer
  # Input Tensor Shape: [batch_size, 1024]
  # Output Tensor Shape: [batch_size, 10]
  logits = tf.layers.dense(inputs=dropout, units=10)
  predictions = {
      # Generate predictions (for PREDICT and EVAL mode)
      "classes": tf.argmax(input=logits, axis=1),
      # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
      # `logging_hook`.
      "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
  }
  if mode == tf.estimator.ModeKeys.PREDICT:
    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
  # Calculate Loss (for both TRAIN and EVAL modes)
  loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
  # Configure the Training Op (for TRAIN mode)
  if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
    train_op = optimizer.minimize(
        loss=loss,
        global_step=tf.train.get_global_step())
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
  # Add evaluation metrics (for EVAL mode)
  eval_metric_ops = {
      "accuracy": tf.metrics.accuracy(
          labels=labels, predictions=predictions["classes"])}
  return tf.estimator.EstimatorSpec(
      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)

def main(unused_argv):
  # Load training and eval data
  mnist = tf.contrib.learn.datasets.load_dataset("mnist")
  train_data = mnist.train.images  # Returns np.array
  train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
  eval_data = mnist.test.images  # Returns np.array
  eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
  # Create the Estimator
  mnist_classifier = tf.estimator.Estimator(
      model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
  # Set up logging for predictions
  # Log the values in the "Softmax" tensor with label "probabilities"
  tensors_to_log = {"probabilities": "softmax_tensor"}
  logging_hook = tf.train.LoggingTensorHook(
      tensors=tensors_to_log, every_n_iter=50)
  # Train the model
  train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
      x={"x": train_data},
      y=train_labels,
      batch_size=100,
      num_epochs=None,
      shuffle=True)
  mnist_classifier.train(
      input_fn=train_input_fn,
      steps=20000,
      hooks=[logging_hook])
  # Evaluate the model and print results
  eval_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
      x={"x": eval_data}, y=eval_labels, num_epochs=1, shuffle=False)
  eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
  print(eval_results)
if __name__ == "__main__":
  tf.app.run()

Answer 1

通常，您只需要在代码中的任何地方指定tf.summary.scalar()，tf.summary.histogram()或tf.summary.image()即可。您可以使用以下方式使用直方图摘要来捕获所有权重和偏见

for value in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
    tf.summary.histogram(value.name, value)

至于可更新的指标摘要，例如F1得分的精度，您需要将其包装在eval_metric_ops中，然后传递给tf.estimator.EstimatorSpec

accuracy = tf.metrics.accuracy(labels=labels, predictions=predictions)
    eval_metric_ops = {'accuracy': accuracy}

1. 您只需用与培训期间指定的同一DIR调用张量板。
2. 您不需要使用tf.summary.merge_all()